Partial Least Squares Research Articles

FTIR Spectroscopic Analysis of Plant Proteins and Correlation with Functional Properties.

Development of plant-based products faces challenges like raw material standardization and time-consuming functionality measurements. Fourier Transform Infrared (FTIR) spectroscopy provides a quick, non-destructive way to analyze protein molecular characteristics. This study explored the classification capability of FTIR in analyzing five plant protein isolates-soy, mung bean, pea, fava bean, and lentil-and assessed its predictive ability for functional property measurement such as water absorption capacity (WAC), oil absorption capacity (OAC), Solubility (SOL), foaming, and emulsification. Functional properties were calculated using traditional methods of measurements. Principal Component Analysis (PCA) and Partial Least Square (PLS) Regression Analysis were used to study FTIR spectra and its correlation with functional properties. PCA revealed distinct clusters for each protein source based on their FTIR spectra, indicating molecular differences. WAC and OAC prediction models showed strong correlations, with prediction correlation coefficients (Rp) of more than 0.99 and cross-validation correlation coefficients (Rcv) ranging from 0.85 to 0.92. As sample size increases, SOL, emulsifying, and foaming properties display promising potential. Moreover, WAC and OAC predictions exhibited robust results with protein blends of various ratios. The expanded WAC model predicted with an Rp of 0.99 and an Rcv of 0.95, while the expanded OAC model had an Rp of 0.99 and an Rcv of 0.84. The results underscore FTIR has the potential to identify plant proteins aiding in raw material verification and quality control as well as being an alternative to analyzing functional properties of plant proteins.

Identification of Parkinson’s disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach

ObjectiveThis study aim to leverage advanced machine learning techniques to develop and validate novel MRI imaging features and single nucleotide polymorphism (SNP) gene data fusion methodologies to enhance the early identification and diagnosis of Parkinson’s disease (PD).MethodsWe leveraged a comprehensive dataset from the Parkinson’s Progression Markers Initiative (PPMI), which includes high-resolution neuroimaging data, genetic single-nucleotide polymorphism (SNP) profiles, and detailed clinical information from individuals with early-stage PD and healthy controls. Two multi-modal fusion strategies were used: feature-level fusion, where we employed a hybrid feature selection algorithm combining Fisher discriminant analysis, an ensemble Lasso (EnLasso) method, and partial least squares (PLS) regression to identify and integrate the most informative features from neuroimaging and genetic data; and decision-level fusion, where we developed an adaptive ensemble stacking (AE_Stacking) model to synergistically integrate the predictions from multiple base classifiers trained on individual modalities.ResultsThe AE_Stacking model achieving the highest average balanced accuracy of 95.36% and an area under the receiver operating characteristic curve (AUC) of 0.974, significantly outperforming feature-level fusion and single-modal models (p < 0.05). Furthermore, by analyzing the features selected across multiple iterations of our models, we identified stable brain region features [lh 6r (FD) and rh 46 (GI)] and key genetic markers (rs356181 and rs2736990 SNPs within the SNCA gene region; rs213202 SNP within the VPS52 gene region), highlighting their potential as reliable early diagnostic indicators for the disease.ConclusionThe AE_Stacking model, trained on MRI and genetic data, demonstrates potential in distinguishing individuals with PD. Our findings enhance understanding of the disease and advance us toward the goal of precision medicine for neurodegenerative disorder.

Optimizing Employee Performance through Compensation, Training, and Work Stress Management in the Indonesian Cement Industry

The Society 5.0 era presents the integration between the physical and digital worlds, giving a significant impact on various sectors, including the workforce. This study aims to analyze the effect of work stress and compensation on employee performance with training as a mediating variable. The research method used is quantitative deductive with data collection through questionnaires on 142 employees of PT Semen Indonesia (Persero) Tbk Gresik. Data analysis was carried out using Partial Least Square (PLS). The results of the study indicate that work stress does not have a significant effect on employee performance or training, while compensation has a positive and significant effect on employee performance and training. Training is proven to have a positive and significant effect on employee performance, as well as being a full mediation between compensation and employee performance. This study recommends companies to improve the work division system, set realistic targets, provide training as needed, and provide psychological counseling for employees to improve performance and reduce work stress.

Pengaruh Kompensasi dan Beban Kerja Terhadap Intention to Quit Karyawan dengan Produktivitas Sebagai Variabel Intervening pada PT. Arta Boga Cemerlang: Unit Depo Medan

This study aims to analyze the effects of compensation and workload on employees' intention to quit, with productivity as an intervening variable, at PT. Arta Boga Cemerlang (Medan Unit). A quantitative method was employed, collecting data via questionnaires distributed to 56 respondents. Data were analyzed using the Structural Equation Modeling (SEM) method based on Partial Least Squares (PLS). The results indicate that compensation has a significant negative effect on intention to quit, while workload has a significant positive effect. Employee productivity was found to mediate the relationship between compensation and intention to quit, as well as between workload and intention to quit. This research provides practical recommendations for company management to effectively manage compensation and workload to enhance productivity and reduce employee intention to quit levels.

Pengaruh Kepuasan Pelanggan dan Kualitas Pelayanan Terhadap Loyalitas Pelanggan Dimediasi oleh Nilai Layanaan yang Dirasakan, Citra Perusahaan, dan Reputasi Perusahaan

This study analyzes the effect of customer satisfaction and service quality on customer loyalty in GO-JEK, mediated by perceived value, corporate image, and corporate reputation. The sample consists of 150 respondents aged over 17 years and Gojek users. Data were collected using a closed-ended questionnaire with a five-point Likert scale and analyzed using Structural Equation Modeling (SEM) based on Partial Least Square (PLS) with SmartPLS 3. The results show that customer satisfaction, service quality, corporate image, and corporate reputation positively affect Gojek customers' perceived value. Corporate image also directly influences customer loyalty, while corporate reputation is not significant. Perceived value enhances customer loyalty. Service quality and corporate image act as mediators in this relationship, whereas customer satisfaction and corporate reputation do not mediate the effect.

PENGARUH PERCEPTION OF ORGANIZATIONAL SUPPORT TERHADAP KOMITMEN AFEKTIF PEGAWAI DIMEDIASI OLEH INTENTION TO STAY PADA PONDOK PESANTREN JABAL NUR KANDIS SIAK

This research aims to determine the relationship between Perception of organizational support and employee Affective Commitment through Intention to Stay at the Jabal Nur Kandis Siak Islamic Boarding School. The research method used is quantitative research. The population in this study were all employees and teachers who worked at the Jabal Nur Kandis Siak Islamic Boarding School. The sampling technique used was purposive sampling, so that a sample of 89 respondents was obtained. The data collection technique used was using a structured questionnaire. The instrument testing technique in this research is validity and reliability testing using PLS (Partial Least Square) as the measurement. The results of this research show that Perception of Organization and Intention to Stay have a direct and significant effect on the Affective Commitment of Employees and Teachers who work at the Jabal Nur Kandis Siak Islamic Boarding School. Likewise, Perception of organizational support has a significant direct effect on Intention to Stay and Intention to Stay has been proven to be able to mediate the relationship between Perception of organizational support and Affective Commitment of Employees and Teachers who work at the Jabal Nur Kandis Siak Islamic Boarding School.

The Influence of Uncertainty, Communication and Employee Passion on Readiness to Change at PT XYZ

Organizational change often brings uncertainty among employees, which can affect their readiness to adapt to the change. In this context, effective communication and employee passion are key factors in reducing uncertainty and increasing readiness to change. PT XYZ, a large manufacturing company in Indonesia, is undergoing a digital transformation through the implementation of a new ERP system aimed at improving work efficiency and effectiveness. This study aims to explore the influence of uncertainty, communication, and passion on employee readiness to change at PT XYZ. The objective of this study is to examine the influence of uncertainty, communication, and employee passion on readiness to change at PT XYZ. This study also aims to identify the most significant factors affecting employee readiness to adapt to the ongoing changes. Thus, it is expected that this research will provide valuable insights for PT XYZ's management in managing change and minimizing employee resistance. Using a quantitative approach, data were collected from 266 employees across various departments and analyzed with the Partial Least Squares (PLS) method. The findings reveal that uncertainty negatively affects readiness to change, while effective communication and employee passion significantly enhance it. Notably, communication effectiveness and employees’ enthusiasm for change emerged as key drivers of organizational adaptability. The study highlights the need for PT XYZ’s management to minimize uncertainty through transparent communication strategies and foster a positive work culture to enhance employee readiness. These insights provide valuable guidance for organizations undergoing similar transformations, emphasizing the critical role of communication and employee engagement in change management success.

Analisis Pengaruh Gaji dan Lingkungan Kerja Terhadap Kinerja Karyawan Melalui Kepuasan Kerja Sebagai Variabel Intervening pada Coffee Shop Kocoba Medan

This study aims to analyze the effect of salary and work environment on employee performance, with job satisfaction as an intervening variable, at Coffee Shop Kocoba Medan. A quantitative approach was employed, with data collected through questionnaires from 35 respondents who are employees of Coffee Shop Kocoba. The data analysis technique used was Structural Equation Modeling (SEM) with a Partial Least Square (PLS) approach. The results show that salary and work environment directly have a positive and significant effect on employee performance. Furthermore, job satisfaction was proven to be an intervening variable that strengthens the relationship between salary and work environment on employee performance. Salary factors, including timely payment and incentives, and a conducive work environment, both physically and non-physically, play a crucial role in improving employee satisfaction and productivity. This research is expected to serve as a reference for the management of Coffee Shop Kocoba in designing more effective human resource policies, particularly regarding salary provision, work environment improvement, and job satisfaction enhancement.

Inversion of Soil Moisture Content in Silage Corn Root Zones Based on UAV Remote Sensing

Accurately monitoring soil moisture content (SMC) in the field is crucial for achieving precision irrigation management. Currently, the development of UAV platforms provides a cost-effective method for large-scale SMC monitoring. This study investigates silage corn by employing UAV remote sensing technology to obtain multispectral imagery during the seedling, jointing, and tasseling stages. Field experimental data were integrated, and supervised classification was used to remove soil background and image shadows. Canopy reflectance was extracted using masking techniques, while Pearson correlation analysis was conducted to assess the linear relationship strength between spectral indices and SMC. Subsequently, convolutional neural networks (CNNs), back-propagation neural networks (BPNNs), and partial least squares regression (PLSR) models were constructed to evaluate the applicability of these models in monitoring SMC before and after removing the soil background and image shadows. The results indicated that: (1) After removing the soil background and image shadows, the inversion accuracy of SMC for CNN, BPNN, and PLSR models improved at all growth stages. (2) Among the different inversion models, the accuracy from high to low was CNN, PLSR, BPNN. (3) From the perspective of different growth stages, the inversion accuracy from high to low was seedling stage, tasseling stage, jointing stage. The findings provide theoretical and technical support for UAV multispectral remote sensing inversion of SMC in silage corn root zones and offer validation for large-scale soil moisture monitoring using remote sensing.

Analysis of Factors affecting Construction Project Tender Winning in Small Qualification Contractors

Finding employment in the construction sector almost always involves a tender process, which is essential for the construction entrepreneurs because the company's continuity depends on it. Tender qualifications and requirements must fulfill several stages, namely the administrative, qualification, technical, and price evaluation. The purpose of this research is to predict the relationship between certain variables. The Partial Least Square (PLS) method is deployed since it can directly analyze the latent variables, indicator variables, and measurement errors and determine the complexity of the relationship between several variables and their indicators. Eight main factors were successfully obtained: Regulations, Company Qualifications, Administration, Equipment Resources, Human Resources, Construction Safety Plan, Financial, and Technology and Information Systems. Using the SEM-PLS also resulted in estimating the significance value of the factors influencing the highest tender winner determinants: Human Resources obtained a value of 84.5% and the Financial variable attained a value of 82.3%. This research is expected to assist contractors in getting acquainted with the level of significance of the tender winners’ determinants for the construction projects. Contractors can also prepare and improve the Human Resources factor by conducting advanced training, certification, and competency development that supports its technical capabilities. Contractors need to obtain a deep understanding of the tender requirements, and with a high experience level they can make more appropriate and competitive offers.

Enhancing prediction of major depressive disorder onset in adolescents: A machine learning approach.

Major Depressive Disorder (MDD) is a prevalent mental health condition that often begins in adolescence, with significant long-term implications. Indicated prevention programs targeting adolescents with mild symptoms have shown efficacy, yet the methods for identifying at-risk individuals need improvement. This study aims to evaluate the utility of Partial Least Squares Regression (PLSR) in predicting the onset of MDD among non-depressed adolescents, compared to traditional screening methods. The study recruited 1462 Portuguese adolescents aged 13-16, who were assessed using various self-report measures and followed for two years. Participants were randomly divided into training (70%, N=1023) and testing (30%, N=439) samples. PLSR models were developed to predict the occurrence of a major depressive episode (MDE) within two years, using 331 variables. The model's performance was compared to the Children's Depression Inventory (CDI) in predicting MDE onset. The best-fitting PLSR model with two components explained 19.1% and 16.9% of the variance in the training and testing samples, respectively, significantly outperforming the CDI, which explained 7.7% of the variance. The area under the ROC curve was 0.78 for PLSR, compared to 0.71 for CDI. An empirically derived cut-off point was used to create dichotomous risk categories, and it showed a significant difference in MDE rates between predicted high-risk and low-risk groups. The balanced accuracy of the PLSR model was 0.77, compared to 0.65 for the CDI method. The PLSR model effectively identified adolescents at risk for developing MDD, demonstrating superior predictive power over the CDI. This study supports the potential utility of ML techniques (e.g., PLSR) in enhancing early identification and prevention efforts for adolescent depression.

Comparison Between the Partial Least Squares Method and Principal Components Using Genetic Algorithm with an Application

The problem of multicollinearity among independent variables in a regression model was addressed in this study using Partial Least Squares (PLS) and Principal Component Analysis (PCA). The influence exerted by multicollinearity can deteriorate the results of regression modeling; that is, it makes the traditional technique OLS less reliable. The research is centered on applying advanced algorithms for Partial Least Squares (SIMPL and O-PLS) as well as for PCA (NIPALS and SVD) on real-life data scenarios, as well as integrating genetic algorithm (GAs) with these algorithms to optimize predictive performance. The relative efficiency of these methods is evaluated primarily through the amplitude of the Mean Square Error (MSE) used as a criterion for comparison. The results show the effectiveness of PLS-OPLS above PCA in terms of the lowest before and after embedding genetic algorithms into the MSE. All this underpins the effectiveness of PLS in minimizing multicollinearity thereby allowing for the formulation and prediction of very highly predictive models. More and more indication of subtle class was revealed with regard to fine tuning advanced GA technique in favor of enhancing regression modeling for complex data analysis. The ongoing research will help in opening numerous possibilities to reinforce regression methodology, especially when one factor in an array of applications representing a relatively significant level of prediction accuracy.

Machine learning driven benchtop Vis/NIR spectroscopy for online detection of hybrid citrus quality.

The aim of this study was to explore application of visible and near-infrared (Vis/NIR) spectroscopy combined with machine learning models for SSC and TA prediction of hybrid citrus. The Vis/NIR spectra of samples including navel-region, equator-region and multi-region combination spectra in navel-region and equator-region were collected using a benchtop equipment. The performance of SSC and TA prediction models with different region spectra, including partial least squares (PLS), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM) and multilayer feedforward neural network (MFNN), was assessed. The accuracy of SSC and TA prediction models with multi-region combination (raw) spectra was better compared to navel-region and equator-region, suggesting that multi-region combination spectra collection method was more suitable. Subsequently, the spectral pre-processing, including Savitzky-Golay smoothing (SGS), maximum normalization (MN), multiplicative scatter correction (MSC), linear baseline correction (LBC) and first derivative (1stD), were performed. The performance of SSC and TA prediction models with different pre-processing spectra was further compared. The PLS with SGS spectra (SGS-PLS) and MFNN with raw spectra (Raw-MFNN) exhibited superior validation effects for SSC and TA prediction, respectively. In a subsequent prediction in new samples, SGS-PLS achieved an RP2 of 0.875, an RMSEP of 0.572% and a MAEP of 0.469% for SSC prediction, and Raw-MFNN achieved an RP2 of 0.800, an RMSEP of 0.0322% and a MAEP of 0.0249% for TA prediction, indicating excellent generalization ability. These results indicate the great potential of benchtop Vis/NIR spectroscopy for online detection of hybrid citrus quality at mass-scale level.

An exploratory study on the development of a sensory wheel affiliated with the emotional lexicon for chrysanthemum infusion.

Chrysanthemum infusion has gained popularity outside of the East Asian market in recent years. However, despite the growing international demand, standardized methods to evaluate the sensory attributes of chrysanthemum tea, which are essential for guiding consumers and ensuring quality control, remain underdeveloped. In this study, a trained panel conducted a quantitative descriptive analysis (QDA®) of eight chrysanthemum samples, successfully distinguishing between them. Additionally, consumer emotional responses to chrysanthemum infusion were assessed using the Check-All-That-Apply (CATA) method. By mapping the descriptive sensory data with emotional response data through Partial Least Squares Regression (PLSR), we identified sensory drivers that elicit specific emotional responses. For this analysis, the emotion lexicon from EsSense25 was clustered into six dimensions: pleasant, tame, warm, active, negative, and bored. Our findings indicate that attributes such as smoothness and chrysanthemum-cucumber flavour induce calm and reassuring emotions (e.g., secure, understanding, calm, and tame). Conversely, sweetness and floral flavour are associated with positive emotions such as happiness, joy, and general well-being, suggesting that floral sweet beverages can mitigate negative emotions. However, sensory attributes such as bitterness, astringency, and vegetal aroma were linked to negative emotions and were sometimes associated with feelings of activity. The development of a sensory wheel, integrated with the emotional lexicon for chrysanthemum infusion, provides a tool for identifying sensory drivers behind emotional experiences. This tool offers valuable insights for market applications and product development, enhancing consumer satisfaction by aligning product attributes with desired emotional outcomes.

Building a near-infrared (NIR) soil spectral dataset and predictive machine learning models using a handheld NIR spectrophotometer.

This near-infrared spectral dataset consists of 2,106 diverse mineral soil samples scanned, on average, on six different units of the same low-cost commercially available handheld spectrophotometer. Most soil samples were selected from the USDA NRCS National Soil Survey Center-Kellogg Soil Survey Laboratory (NSSC-KSSL) soil archives to represent the diversity of mineral soils (0-30 cm) found in the United States, while 90 samples were selected from Ghana, Kenya, and Nigeria to represent available African soils in the same archive. All scanning was performed on dried and sieved (<2 mm) soil samples. Machine learning predictive models were developed for soil organic carbon (SOC), pH, bulk density (BD), carbonate (CaCO3), exchangeable potassium (Ex. K), sand, silt, and clay content from their spectra in the R programming language using most of this dataset (1,976 US soils) and are included in this data release. Two model types, Cubist and partial least squares regression (PLSR) were developed using two strategies: (1) using an average of the spectral scans across devices for each sample and, (2) using the replicate spectral scans across devices for each sample. We present the internal performance of these models here. The dry spectra and Cubist models for these soil properties are available for download from 10.5281/zenodo.7586621. An example of detailed code used to produce these models is hosted at the Open Soil Spectral Library, a free service of the Soil Spectroscopy for the Global Good Network (soilspectroscopy.org), enabling broad use of these data for multiple soil monitoring applications.

Quantitative Analysis of Peanut Skin Adulterants by Fourier Transform Near-Infrared Spectroscopy Combined with Chemometrics

Peanut skin is a potential medicinal material. The adulteration of peanut skin samples with starchy substances severely affects their medicinal value. This study aimed to quantitatively analyze the adulterants present in peanut skin using Fourier transform near-infrared (FT-NIR) spectroscopy. Two adulterants, sweet potato starch and corn starch, were included in this study. First, spectral information of the adulterated samples was collected for characterization. Then, the applicability of different preprocessing methods and techniques to the obtained spectral data was compared. Subsequently, the Competitive Adaptive Reweighted Sampling (CARS) algorithm was used to extract effective variables from the preprocessed spectral data, and Partial Least Squares Regression (PLSR), a Support Vector Machine (SVM), and a Black Kite Algorithm-Support Vector Machine (BKA-SVM) were employed to predict the adulterant content in the samples, as well as the overall adulteration level. The results showed that the BKA-SVM model performed excellently in predicting the content of sweet potato starch, corn starch, and overall adulterants, with determination coefficients (RP2) of 0.9833, 0.9893, and 0.9987, respectively. The experimental results indicate that FT-NIR spectroscopy combined with advanced machine learning techniques can effectively and accurately detect adulterants in peanut skin, providing a reliable technological support for food safety detection.

Unravelling critical climatic factors and phenological stages impacting spring barley yields across Europe

Yield is a complex trait reflecting the interaction between genotype, environment and farm management. The challenge of adapting spring crops to climate change involves unravelling the contribution of climatic factors that impact yield performance according to phenological stages. The aim of the present study was to identify the main Environmental Covariates (EC) – climatic variables calculated over phenological stages – driving spring barley yield levels. Five contrasting European agro-climatic (AC) regions were defined as follows: United Kingdom and Ireland (UK-IE), Denmark and Sweden (DK-SE), France (FR), Northeast Germany, Czech Republic and Poland (N.E. DE-CZ-PL) and South Germany and Austria (S. DE-AT). Yield data from 270 two-row spring barley accessions/varieties, grown in 125 environments between 2015 and 2021, were collected from a multi-environment trials network. Using the phenology-calibrated CERES-Barley model (DSSAT), 91 ECs were calculated in each environment based on collected weather data and simulated phenological stages. Partial Least Squares (PLS) regression analyses were carried out to sort out the main ECs impacting yield performance in each of the five AC regions. Results showed that elevated temperatures and solar radiation were the main yield-drivers in all AC regions. Associations between water availability and yield were detected in most AC regions. The strongest contrasts were observed for the critical phenological stages during pre-anthesis, which govern grain number per unit area. Cool temperatures (days with minimum temperature <0°C or <7°C and average temperature <15°C) during emergence and tillering, and solar radiation intensity between emergence and grain filling, were the most yield contributing ECs. This study showed the importance of considering climate during early stages to predict yields. The identification of major yields EC drivers suggests the need to adjust agricultural practices in spring barley production across Europe for climate adaptation. This study unraveled the complexity of yield ecophysiology affecting spring barley in Europe. In order to improve the adaptation of spring barley to climate change, the perspective is to examine the role of ECs on genotype x environment interactions for yield and develop stable cultivars that outperform existing germplasm.

Intelligent consensus-based predictions of early life stage toxicity in fish tested in compliance with OECD Test Guideline 210.

Early life stage (ELS) toxicity testing in fish is a crucial test procedure used to evaluate the long-term effects of a wide range of chemicals, including pesticides, industrial chemicals, pharmaceuticals, and food additives. This test is particularly important for screening and prioritizing thousands of chemicals under the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) regulation. In silico methods can be used to estimate the toxicity of a chemical when no experimental data is available and to reduce the cost, time, and resources involved in the experimentation process. In the present study, we developed predictive Quantitative Structure-Activity Relationship (QSAR) models to assess chronic effects of chemicals on ELS in fish. Toxicity data for ELS in fish was collected from two different sources, i.e. J-CHECK and eChemPortal, which contain robust study summaries of experimental studies performed according to OECD Test Guideline 210. The collected data included two types of endpoints - the No Observed Effect Concentration (NOEC) and the Lowest Observed Effect Concentration (LOEC), which were utilized to develop the QSAR models. Six different partial least squares (PLS) models with various descriptor combinations were created for both endpoints. These models were then employed for intelligent consensus-based prediction to enhance predictability for unknown chemicals. Among these models, the consensus model - 3 (Q2F1 = 0.71, Q2F2 = 0.71) and individual model - 3 (Q2F1 = 0.80, Q2F2 = 0.79) exhibited most promising results for both the NOEC and LOEC endpoints. Furthermore, these models were validated experimentally using experimental data from nine different industrial chemicals provided by Global Product Compliance (Europe) AB. Lastly, the models were used to screen and prioritize chemicals obtained from the Pesticide Properties (PPDB) and DrugBank databases.

Unraveling clay-mineral genesis and climate change on Earth and Mars using machine learning-based VNIR spectral modeling

Abstract Clay minerals are common in martian geological units and are globally widespread on Earth. Understanding the origin, formation, and alteration of clay minerals is crucial for unraveling past environmental conditions on Earth and Mars, in which the composition and crystallinity of clay minerals serve as important surrogate indicators for addressing these issues. Here, 621 soil and sediment samples from five chronosequences representing different climatic zones of China were investigated using visible to near-infrared reflectance (VNIR) in combination with X-ray diffraction (XRD) analysis. The crystallinity of clay minerals (i.e., illite crystallinity, illite chemistry index, kaolinite crystallinity) and clay mineral alteration index (CMAI) were analyzed with conventional methods and then predicted through a spectral modeling approach. Our results show that kaolinite with a pedogenic or sedimentary origin is characterized by a broad crystallinity range and a poorly ordered structure, especially when generated in an intense weathering environment. Predictive models were constructed with data-mining methods, including partial least-squares regression (PLSR), random forest (RF), and Cubist algorithms. The predictive performance of the crystallinity and CMAI proxies is robust, with an overall accuracy of 78% and a residual prediction deviation (RPD) of 2.57. We also found that the model’s accuracy in predicting clay-mineral-related proxies increased by 45% using random forest (RF) and Cubist compared to the PLSR models. We suggest that VNIR spectroscopy combined with RF and Cubist methods has the potential to be an alternative and broadly applicable tool for analyzing typical clay-mineral proxies, substituting for a series of common mineralogic analyses. Spectral modeling can reveal genetic and climatic information at both field and regional scales, which has profound implications for Mars missions and other space exploration programs.

A comparative study on rapid qualitative and quantitative determination of olive oil adulteration.

Authenticity of olive oil is a significant concern for producers, consumers, and policymakers. To help address this issue, a rapid, efficient, and accurate flow injection mass spectrometric (FIMS) fingerprinting approach, combined with SVM and PLS classification and regression models, was proposed for the identification and quantitative analysis of olive oil adulteration. Based on the comprehensive comparative analysis, SVM outperformed those of PLS-DA, achieving higher values for accuracy, sensitivity, and specificity, as well as positive predictive and negative predictive values, in identifying adulterated olive oil samples. Furthermore, compared with PLSR model, the SVR model demonstrated superior performance in determining the content of adulterated olive oil, with a higher coefficient of determination and lower Root Mean Square Error. In conclusion, FIMS fingerprinting technology in combination with SVM can be effectively implemented for rapid, reliable, and accurate identification and quantification of olive oil adulteration.